Comparing churn prediction techniques and assessing their performance: a contingent perspective

Tamaddoni, Ali, Stakhovych, Stanislav and Ewing, Michael 2016, Comparing churn prediction techniques and assessing their performance: a contingent perspective, Journal of service research, vol. 19, no. 2, pp. 123-141, doi: 10.1177/1094670515616376.

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Title Comparing churn prediction techniques and assessing their performance: a contingent perspective
Author(s) Tamaddoni, AliORCID iD for Tamaddoni, Ali orcid.org/0000-0002-5943-3172
Stakhovych, Stanislav
Ewing, MichaelORCID iD for Ewing, Michael orcid.org/0000-0002-2260-2761
Journal name Journal of service research
Volume number 19
Issue number 2
Start page 123
End page 141
Total pages 19
Publisher Sage
Place of publication London, Eng.
Publication date 2016-05
ISSN 1094-6705
1552-7379
Keyword(s) customer churn
prediction
profitability
retention
probability models
data mining
simulations
Summary Customer retention has become a focal priority. However, the process of implementing an effective retention campaign is complex and dependent on firms’ ability to accurately identify both at-risk customers and those worth retaining. Drawing on empirical and simulated data from two online retailers, we evaluate the performance of several parametric and nonparametric churn prediction techniques, in order to identify the optimal modeling approach, dependent on context. Results show that under most circumstances (i.e., varying sample sizes, purchase frequencies, and churn ratios), the boosting technique, a nonparametric method, delivers superior predictability. Furthermore, in cases/contexts where churn is more rare, logistic regression prevails. Finally, where the size of the customer base is very small, parametric probability models outperform other techniques.
Language eng
DOI 10.1177/1094670515616376
Field of Research 150505 Marketing Research Methodology
1505 Marketing
1504 Commercial Services
Socio Economic Objective 910403 Marketing
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Sage
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082975

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